| Texto completo | |
| Autor(es): |
Carneiro, Murillo G.
;
Zhao, Liang
;
Cheng, Ran
;
Jin, Yaochu
;
IEEE
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2016-01-01. |
| Resumo | |
While most part of the complex network models are described in function of some growth mechanism, the optimization of a goal or certain characteristics can be desirable for some problems. This paper investigates structural optimization of networks in the highlevel classification context, where the classification produced by a traditional classifier is combined with the classification provided by complex network measures. Using the recently proposed social learning particle swarm optimization (SL-PSO), a bio-inspired optimization framework, which is responsible to build up the network and adjust the parameters of the hybrid model while conducting the optimization of a quality function, is proposed. Experiments on two real-world problems, the Handwritten Digits Recognition and the Semantic Role Labeling (SRL), were performed. In both problems, the optimization framework is able to improve the classification given by a state-of-the-art algorithm to SRL. Furthermore, the optimization framework proposed here can be extended to other machine learning tasks. (AU) | |
| Processo FAPESP: | 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria |
| Beneficiário: | Francisco Louzada Neto |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |
| Processo FAPESP: | 11/50151-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações |
| Beneficiário: | Elbert Einstein Nehrer Macau |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 12/07926-3 - Algoritmos Evolutivos para Anotação de Papéis Semânticos |
| Beneficiário: | Murillo Guimarães Carneiro |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |